class_docstr: When called, this ensemble will return the mean prediction from the entire list of underlying mixers.
  
  NOTE: can only be used in regression tasks.
code: def __init__(
      self,
      target,
      mixers: List[BaseMixer],
      data: EncodedDs,
      dtype_dict: dict,
      fit: Optional[bool] = True,
      **kwargs,
  ) -> None:
      super().__init__(target, mixers, data, fit=False)
      if dtype_dict[target] not in (
          dtype.float,
          dtype.integer,
          dtype.quantity,
      ):
          raise Exception(
              f"This ensemble can only be used regression problems! Got target dtype {dtype_dict[target]} instead!"
          )
  
file_path: lightwood/lightwood/ensemble/mean_ensemble.py
imports: from typing import List, Optional
  import pandas
  from lightwood.mixer.base import BaseMixer
  from lightwood.ensemble.base import BaseEnsemble
  from lightwood.api.types import PredictionArguments
  from lightwood.data.encoded_ds import EncodedDs
  from type_infer.dtype import dtype
label_desc: Use image node_id 1 to create a new MeanEnsemble object from inherited base classes: BaseEnsemble with example: obj = MeanEnsemble(target, mixers, data, dtype_dict, fit)